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  2. Jorge Nocedal - Wikipedia

    en.wikipedia.org/wiki/Jorge_Nocedal

    Nocedal is well-known for his research in nonlinear optimization, particularly for his work on L-BFGS [4] [5] and his textbook Numerical Optimization. [6] In 2001, Nocedal co-founded Ziena Optimization Inc. and co-developed the KNITRO software package. [7] Nocedal was a chief scientist at Ziena Optimization Inc. from 2002 to 2012 before the ...

  3. Active-set method - Wikipedia

    en.wikipedia.org/wiki/Active-set_method

    For example, in solving the linear programming problem, the active set gives the hyperplanes that intersect at the solution point. In quadratic programming , as the solution is not necessarily on one of the edges of the bounding polygon, an estimation of the active set gives us a subset of inequalities to watch while searching the solution ...

  4. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    Such a formulation is called an optimization problem or a mathematical programming problem (a term not directly related to computer programming, but still in use for example in linear programming – see History below). Many real-world and theoretical problems may be modeled in this general framework.

  5. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    Nocedal, Jorge and Wright, Stephen J. (1999). Numerical Optimization. Springer. ISBN 0-387-98793-2. Jan Brinkhuis and Vladimir Tikhomirov, Optimization: Insights and Applications, 2005, Princeton University Press

  6. Augmented Lagrangian method - Wikipedia

    en.wikipedia.org/wiki/Augmented_Lagrangian_method

    Augmented Lagrangian methods are a certain class of algorithms for solving constrained optimization problems. They have similarities to penalty methods in that they replace a constrained optimization problem by a series of unconstrained problems and add a penalty term to the objective, but the augmented Lagrangian method adds yet another term designed to mimic a Lagrange multiplier.

  7. Sequential quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Sequential_quadratic...

    Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization which may be considered a quasi-Newton method.SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable, but not necessarily convex.

  8. Quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Quadratic_programming

    Quadratic programming in MATLAB requires the Optimization Toolbox in addition to the base MATLAB product Mathematica: A general-purpose programming-language for mathematics, including symbolic and numerical capabilities. MOSEK: A solver for large scale optimization with API for several languages (C++, Java, .Net, Matlab and Python). NAG ...

  9. Rate of convergence - Wikipedia

    en.wikipedia.org/wiki/Rate_of_convergence

    One class of examples is the staggered geometric progressions that get closer to their limits only every other step or every several steps, for instance the example () =,, /, /, /, /, …, / ⌊ ⌋, … detailed below (where ⌊ ⌋ is the floor function applied to ). The defining Q-linear convergence limits do not exist for this sequence ...